Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles
Authors: Niklas Åkerblom, Yuxin Chen, Morteza Haghir Chehreghani
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the performance of our methods via several real-world experiments on Luxembourg SUMO Traffic dataset. 5 Experimental Results |
| Researcher Affiliation | Collaboration | Niklas Akerblom1,3 , Yuxin Chen2 and Morteza Haghir Chehreghani3 1Volvo Car Corporation 2The University of Chicago 3Chalmers University of Technology |
| Pseudocode | Yes | Algorithm 1 Online learning for energy-efficient navigation; Algorithm 2 Gaussian parameter update of the energy model; Algorithm 3 Thompson Sampling; Algorithm 4 Bayes UCB |
| Open Source Code | No | The paper does not provide any links to source code or state that its code is publicly available. |
| Open Datasets | Yes | We utilize the Luxembourg SUMO Traffic (Lu ST) Scenario data [Codec a et al., 2017] to provide realistic traffic patterns and vehicle speed distributions for each hour of the day. |
| Dataset Splits | No | The paper describes its online learning framework and experiments over a 'horizon' of sessions, but it does not specify traditional train/validation/test dataset splits or cross-validation details for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using 'Luxembourg SUMO Traffic (Lu ST) Scenario data' and extending a 'simulation framework' but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, SUMO versions). |
| Experiment Setup | Yes | We use the default vehicle parameters that were provided for the energy consumption model in [Basso et al., 2019], with vehicle frontal surface area A = 8 meters, air drag coefficient Cd = 0.7 and rolling resistance coefficient Cr = 0.0064. The vehicle is a medium duty truck with vehicle mass m = 14750 kg... We set ϕ = 0.1 for both. For the prior... σ2 0 = (ϑµ0(e))2, where ϑ = 0.25. We run the simulations with a horizon of T = 400 (i.e., T = 400 sessions). |